SoroushVahidi/scidocs huggingface dataset:
Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-vehicle Network
(2018)

Modern vehicles are equipped with Electronic Control Units (ECUs) and external communication devices. The Controller Area Network (CAN), a widely used communication protocol for ECUs, does not have a security mechanism to detect improper packets; if attackers exploit the vulnerability of an ECU and manage to inject a malicious message, they are able to control other ECUs to cause improper operation of the vehicle. With the increasing popularity of connected cars, it has become an urgent matter to protect in-vehicle networks against security threats. In this paper, we study the applicability of statistical anomaly detection methods for identifying malicious CAN messages in invehicle networks. We focus on intrusion attacks of malicious messages. Because the occurrence of an intrusion attack certainly influences the message traffic, we focus on the number of messages observed in a fixed time window to detect intrusion attacks. We formalize features to represent a message sequence that incorporates the number of messages associated with each receiver ID. We collected CAN message data from an actual vehicle and conducted a quantitative analysis of the methods and the features in practical situations. The results of our experiments demonstrated our proposed methods provide fast and accurate detection in various cases.
PCN: Point Completion Network
(2018)

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.
Identification of extremist videos in online video sharing sites
(2009)

Web 2.0 has become an effective grassroots communication platform for extremists to promote their ideas, share resources, and communicate among each other. As an important component of Web 2.0, online video sharing sites such as YouTube and Google video have also been utilized by extremist groups to distribute videos. This study presented a framework for identifying extremist videos in online video sharing sites by using user-generated text content such as comments, video descriptions, and titles without downloading the videos. Text features including lexical features, syntactic features and content specific features were first extracted. Then Information Gain was used for feature selection, and Support Vector Machine was deployed for classification. The exploratory experiment showed that our proposed framework is effective for identifying online extremist videos, with the F-measure as high as 82%.
A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms
(2018)

Text summarization is the process of producing a shorter version of a specific text. Automatic summarization techniques have been applied to various domains such as medical, political, news, and legal domains proving that adapting domain-relevant features could improve the summarization performance. Despite the existence of plenty of research work in the domain-based summarization in English and other languages, there is a lack of such work in Arabic due to the shortage of existing knowledge bases. In this paper, a hybrid, single-document text summarization approach (abbreviated as (ASDKGA)) is presented. The approach incorporates domain knowledge, statistical features, and genetic algorithms to extract important points of Arabic political documents. The ASDKGA approach is tested on two corpora KALIMAT corpus and Essex Arabic Summaries Corpus (EASC). The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework was used to compare the automatically generated summaries by the ASDKGA approach with summaries generated by humans. Also, the approach is compared against three other Arabic text summarization approaches. The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.
Ring-LWE Ciphertext Compression and Error Correction: Tools for Lightweight Post-Quantum Cryptography
(2016)

Some lattice-based public key cryptosystems allow one to transform ciphertext from one lattice or ring representation to another efficiently and without knowledge of public and private keys. In this work we explore this lattice transformation property from cryptographic engineering viewpoint. We apply ciphertext transformation to compress Ring-LWE ciphertexts and to enable efficient decryption on an ultra-lightweight implementation targets such as Internet of Things, Smart Cards, and RFID applications. Significantly, this can be done without modifying the original encryption procedure or its security parameters. Such flexibility is unique to lattice-based cryptography and may find additional, unique real-life applications.
 Ciphertext compression can significantly increase the probability of decryption errors. We show that the frequency of such errors can be analyzed, measured and used to derive precise failure bounds for n-bit error correction. We introduce XECC, a fast multi-error correcting code that allows constant time implementation in software.
 We use these tools to construct and explore TRUNC8, a concrete Ring-LWE encryption and authentication system. We analyze its implementation, security, and performance. We show that our lattice compression technique reduces ciphertext size by more than 40% at equivalent security level, while also enabling public key cryptography on previously unreachable ultra-lightweight platforms.
 The experimental public key encryption and authentication system has been implemented on an 8-bit AVR target, where it easily outperforms elliptic curve and RSA-based proposals at similar security level. Similar results have been obtained with a Cortex M0 implementation. The new decryption code requires only a fraction of the software footprint of previous Ring-LWE implementations with the same encryption parameters, and is well suited for hardware implementation.
LEARNING ENGLISH IS FUN VIA KAHOOT : STUDENTS ’ ATTITUDE , MOTIVATION AND PERCEPTIONS
(2017)

This study is aimed to investigate primary level students’ attitude and motivation and perceptions in learning English using Kahoot game. According to current development in Malaysian education field, 21st century learning style is more focused and given importance. Thus, Kahoot, game-based learning platform has been used to stimulate students’ from a rural area in southern Malaysia to learn English as their second language more effectively, actively and interestingly. The research design for this study is action research. Nine students were chosen as the target group for this study. The Kahoot game was conducted for three different topics in English after they completed the lesson each day. The data was collected using Attitude and Motivation Test Battery questionnaires consisting of 10 items, interview session with 5 semi-structured questions for three selected students and results of each Kahoot game played by the students. The data was analysed using descriptive analysis. The results of the questionnaires, interview session and results of the game were shown in figures and tables. Findings of the study show that all the nine students were able to engage actively in the game and they were able to master the target language effectively. They enjoy learning English using games and wish to have more games in future.
A Simplified Iron Loss Model for Laminated Magnetic Cores
(2008)

This paper presents a new method for the prediction of iron losses in laminated magnetic cores. The method is based on the evaluation of the magnetization curves and loop shapes by which the iron losses, including hysteresis, excess, and classical eddy-current losses, are calculated from the loop area. The purpose of the method is to omit the numerical solution of the nonlinear diffusion equation (resulting from Maxwell equations) while maintaining reasonable accuracy of the iron losses. Therefore, the proposed method determines, using simple concepts, three magnetic field strength components that are responsible for generating the iron losses. The magnetic field component of the hysteresis loss is calculated by a static hysteresis model. The magnetic field component of the excess loss is calculated using the statistical loss theory. Determining the magnetic field component of the classical eddy-current loss is based upon the assumption that the flux distribution in the steel sheet is uniform, but, here, the eddy-current loss is implicitly enforced to be dependent on the magnetic characteristics of the material. The proposed method is applied to the calculation of iron losses in an electrical steel when the accuracy, stability, and generality of the method are discussed.
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
(2018)

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e.g. hours vs. days) because they parallelize better. However, many RL problems require directed exploration because they have reward functions that are sparse or deceptive (i.e. contain local optima), and it is unknown how to encourage such exploration with ES. Here we show that algorithms that have been invented to promote directed exploration in small-scale evolved neural networks via populations of exploring agents, specifically novelty search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to improve its performance on sparse or deceptive deep RL tasks, while retaining scalability. Our experiments confirm that the resultant new algorithms, NS-ES and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES to achieve higher performance on Atari and simulated robots learning to walk around a deceptive trap. This paper thus introduces a family of fast, scalable algorithms for reinforcement learning that are capable of directed exploration. It also adds this new family of exploration algorithms to the RL toolbox and raises the interesting possibility that analogous algorithms with multiple simultaneous paths of exploration might also combine well with existing RL algorithms outside ES.
GlobeSen: an open interconnection framework based on named sensory date for IoT
(2017)

How to efficiently interconnect ubiquitous wireless sensor networks (WSNs) and the Internet becomes an important challenge of Internet of Things. In this paper, we explore a route of information-centric networking (ICN) to solve this challenge and propose an open interconnection framework named GlobeSen. To overcome the problem that traditional ICN solution (such as NDN) is not suitable for resource-constrained WSNs, we present a new implementation of NDN, NDNs, for WSNs. Specifically, by extracting the spatio-temporal information and data type information of interest, we construct a globally unique name structure, and exploit the spatio-temporal relation operation as the matching method. Based on the new naming strategy and matching method, we further design packet forwarding and routing schemes. Moreover, we also develop a gateway system, NDNt, for protocol translating and an application, SenBrowser, to provide a user-friendly interface for generating interests and illustrating the returned sensory data. We implement a proof-of-concept prototype based on TelosB sensor nodes and an ARM development board, and conduct a series of experiments to evaluate the performance of GlobeSen.
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem
(2002)

The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.
Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects
(2002)

Both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DTMO) play key roles in robotics and automation. For certain constrained environments, SLAM and DTMO are becoming solved problems. But for robots working outdoors, and at high speeds, SLAM and DTMO are still incomplete. In earlier works, SLAM and DTMO are treated as two separate problems. In fact, they can be complementary to one another. In this paper, we present a new method to integrate SLAM and DTMO to solve both problems simultaneously for both indoor and outdoor applications. The results of experiments carried out with CMU Navlab8 and Navlab11 vehicles with the maximum speed of 45 mph in crowded urban and suburban areas verify the described work.
Credibilist occupancy grids for vehicle perception in dynamic environments
(2011)

In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the world-fixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.
An evidential approach to map-building for autonomous vehicles
(1998)

In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are “fused” into the map using the Dempster–Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.
Toward Abstractive Summarization Using Semantic Representations
(2015)

We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
MANAGING HEALTH CARE SUPPLY CHAIN : TRENDS , ISSUES , AND SOLUTIONS FROM A LOGISTICS PERSPECTIVE
(2004)

The U.S. healthcare industry is a large enterprise accounting for over 14.1% of the national economic output in 2001. It has been under pressure for cost containment and providing quality health care services to consumers. Its record of investing heavily on development of sophisticated drugs and diagnostic systems does not match that of technologies to manage its day-to-day operations. In order to achieve improved performance, healthcare supply chain must be efficient and integrated. The driver for this integration is logistics and supply chain management. This paper describes trends, issues and some solutions for logistics management for Health Care Supply Chain with concepts drawn from Industrial Engineering, and Operations Research disciplines applied to specific domains. A healthcare supply chain template utilizing Ecommerce strategy is presented. Use of simulation, optimization, and information sharing techniques are demonstrated to optimize purchasing and inventory policies. (
A study on peer startup process and initial offset placement in P2P live streaming systems
(2008)

In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.
Rectal mucosal dissection commencing directly on the anorectal line versus commencing above the dentate line in laparoscopy-assisted transanal pull-through for Hirschsprung's disease: Prospective medium-term follow-up.
(2015)

BACKGROUND
In 2007, we began using the anorectal line (ARL) as the landmark for commencing rectal mucosal dissection (RMD) instead of the dentate line (DL) during laparoscopy-assisted transanal pull-through (L-TAPT) for Hirschsprung's disease (HD). We conducted a medium-term prospective comparison of postoperative fecal continence (POFC) between DL and ARL cases to follow our short-term study.


METHODS
POFC is assessed by scoring frequency of motions, severity of staining, severity of perianal erosions, anal shape, requirement for medications, sensation of rectal fullness, and ability to distinguish flatus from stool on a scale of 0 to 2 (maximum: 14).


RESULTS
Patient demographics were similar for ARL (2007-2014: n=33) and DL (1997-2006: n=41). There were no intraoperative complications and 2 cases of postoperative colitis in both ARL (6.1%) and DL (4.9%). Mean annual medium-term POFC scores for the 4-7 term of this study were consistently better in ARL: 9.7±1.4*, 10.1±1.6*, 10.6±1.6, and 11.3±1.4* in ARL and 8.6±1.5, 9.1±1.6, 9.8±1.9, 10.0±1.6 in DL (*: p<0.05).


CONCLUSIONS
Medium-term POFC is better when the ARL is used as the landmark for RMD during L-TAPT for HD.
Xpander: Towards Optimal-Performance Datacenters
(2016)

Despite extensive efforts to meet ever-growing demands, today's datacenters often exhibit far-from-optimal performance in terms of network utilization, resiliency to failures, cost efficiency, incremental expandability, and more. Consequently, many novel architectures for high-performance datacenters have been proposed. We show that the benefits of state-of-the-art proposals are, in fact, derived from the fact that they are (implicitly) utilizing "expander graphs" (aka expanders) as their network topologies, thus unveiling a unifying theme of these proposals. We observe, however, that these proposals are not optimal with respect to performance, do not scale, or suffer from seemingly insurmountable deployment challenges. We leverage these insights to present Xpander, a novel datacenter architecture that achieves near-optimal performance and provides a tangible alternative to existing datacenter designs. Xpander's design turns ideas from the rich graph-theoretic literature on constructing optimal expanders into an operational reality. We evaluate Xpander via theoretical analyses, extensive simulations, experiments with a network emulator, and an implementation on an SDN-capable network testbed. Our results demonstrate that Xpander significantly outperforms both traditional and proposed datacenter designs. We discuss challenges to real-world deployment and explain how these can be resolved.
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
(2018)

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the CnnRnn Earthquake Detector (CRED), a detector based on deep neural networks. The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the timefrequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with a F-score of 99.95. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.